
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


SCREAMING_SNAKE_CASE__  :     List[str]   				=       logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer

					class   lowerCAmelCase__	(      lowercase__     ):






							def __init__(						self						:				int				,     SCREAMING_SNAKE_CASE__						:				int = 1_01    )	->				List[str]:
											__lowerCamelCase          =      length






							def __len__(						self						:				Any    )	->				List[Any]:
											return self.length







							def __getitem__(						self						:				str				,     SCREAMING_SNAKE_CASE__						:				Union[str, Any]    )	->				int:
											return i





					class   lowerCAmelCase__	:






							def __call__(						self						:				List[Any]				,     SCREAMING_SNAKE_CASE__						:				List[Any]    )	->				Optional[Any]:
											return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE_    ), "labels": torch.tensor(SCREAMING_SNAKE_CASE_    )}





					class   lowerCAmelCase__	(      nn.Module     ):






							def __init__(						self						:				List[str]    )	->				List[Any]:
											super().__init__()
											# Add some (unused) params otherwise DDP will complain.
											__lowerCamelCase          =      nn.Linear(1_20				,     80    )







							def 					__A			(						self						:				Optional[int]				,     SCREAMING_SNAKE_CASE__						:				str				,     SCREAMING_SNAKE_CASE__						:				Optional[int]=None    )	->				Union[str, Any]:


											if labels is not None:
															return torch.tensor(0.0				,     device=input_ids.device    ), input_ids
											else:
															return input_ids

class   lowerCAmelCase__	(      lowercase__     ):






		@require_torch_neuroncore
		def 					__A			(						self						:				Union[str, Any]    )	->				Union[str, Any]:
						__lowerCamelCase          =      f'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
						__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
						__lowerCamelCase          =      f'''--output_dir {output_dir}'''.split()
						__lowerCamelCase          =      ['''torchrun'''] + distributed_args + args
						execute_subprocess_async(SCREAMING_SNAKE_CASE_				,     env=self.get_env()    )
						# successful return here == success - any errors would have caused an error in the sub-call





class   lowerCAmelCase__	(      lowercase__     ):






		@require_torch_multi_gpu
		def 					__A			(						self						:				List[str]    )	->				List[str]:
						__lowerCamelCase          =      f'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
						__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
						__lowerCamelCase          =      f'''--output_dir {output_dir}'''.split()
						__lowerCamelCase          =      ['''torchrun'''] + distributed_args + args
						execute_subprocess_async(SCREAMING_SNAKE_CASE_				,     env=self.get_env()    )
						# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					SCREAMING_SNAKE_CASE__  :     Any   				=       HfArgumentParser((TrainingArguments,))
					SCREAMING_SNAKE_CASE__  :     List[str]   				=       parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '
					    F'distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [101, 40, 7]:
										SCREAMING_SNAKE_CASE__  :     Tuple   				=       DummyDataset(dataset_length)




										def 					__magic_name__					(  __lowerCAmelCase      :		EvalPrediction				)     ->  Dict:
														__lowerCamelCase          =      list(range(len(_lowerCamelCase				)				)				)
														__lowerCamelCase          =      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
														if not success and training_args.local_rank == 0:
																		logger.warning(
																		    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
																		    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''				)
														return {"success": success}

										SCREAMING_SNAKE_CASE__  :     List[Any]   				=       Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										SCREAMING_SNAKE_CASE__  :     str   				=       trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										SCREAMING_SNAKE_CASE__  :     Tuple   				=       trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										SCREAMING_SNAKE_CASE__  :     Optional[int]   				=       2

										SCREAMING_SNAKE_CASE__  :     Tuple   				=       trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										SCREAMING_SNAKE_CASE__  :     List[str]   				=       trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)







										SCREAMING_SNAKE_CASE__  :     Optional[Any]   				=       None





"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	str:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[int]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	int      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	int      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	Dict:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Optional[int]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	List[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Tuple      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Optional[Any]      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)


